class_docstr: Encode categorical integer features using a one-hot aka one-of-K scheme.
  
  The input to this transformer should be a sparse matrix of integers, denoting
  the values taken on by categorical (discrete) features. The output will be
  a sparse matrix were each column corresponds to one possible value of one
  feature. It is assumed that input features take on values in the range
  [0, n_values).
  
  Attributes
  ----------
  `feature_indices_` : array of shape (n_features,)
      Indices to feature ranges.
      Feature ``i`` in the original data is mapped to features
      from ``feature_indices_[i]`` to ``feature_indices_[i+1]``
      (and then potentially masked by `active_features_` afterwards)
  
  `n_values_` : array of shape (n_features,)
      Maximum number of values per feature.
code: def fit_transform(self, X, y=None):
      X = self._check_X(X)
  
      n_samples, n_features = X.shape
      n_values = X.max(axis=0).toarray().flatten() + 2
      self.n_values_ = n_values
      n_values = np.hstack([[0], n_values])
      indices = np.cumsum(n_values)
      self.feature_indices_ = indices
  
      row_indices = X.indices
      column_indices = []
      for i in range(len(X.indptr) - 1):
          nbr = X.indptr[i + 1] - X.indptr[i]
          column_indices_ = [indices[i]] * nbr
          column_indices_ += X.data[X.indptr[i] : X.indptr[i + 1]]
          column_indices.extend(column_indices_)
      data = np.ones(X.data.size)
  
      out = sparse.coo_matrix(
          (data, (row_indices, column_indices)),
          shape=(n_samples, indices[-1]),
          dtype=np.int32,
      ).tocsc()
  
      mask = np.array(out.sum(axis=0)).ravel() != 0
      active_features = np.where(mask)[0]
      out = out[:, active_features]
      self.active_features_ = active_features
      return out.tocsr()
  
file_path: auto-sklearn/autosklearn/pipeline/implementations/SparseOneHotEncoder.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
label_desc: Use image node_id 3 for calling the SparseOneHotEncoder obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: out